Canonical Variate Nonlinear Principal Component Analysis for Monitoring Nonlinear Dynamic Processes
Author:
Affiliation:
1. School of Electrical Engineering, Nantong University
2. College of Information Science and Engineering, Northeastern University
Publisher
Society of Chemical Engineers, Japan
Subject
General Chemical Engineering,General Chemistry
Link
https://www.jstage.jst.go.jp/article/jcej/55/1/55_19we080/_pdf
Reference27 articles.
1. Alcala, C. F. and S. J. Qin; “Reconstruction-Based Contribution for Process Monitoring With Kernel Principal Component Analysis,” Ind. Eng. Chem. Res., 49, 7849–7857 (2010)
2. Chakour, C., M.-F. Harkat and M. Djeghaba; “Neuronal Principal Component Analysis for Nonlinear Time-Varying Processes Monitoring,” IFAC-PapersOnLine, 48, 1408–1413 (2015)
3. Chiang, L. H., R. D. Braatz and E. Russell; Fault Detection and Diagnosis in Industrial Systems, pp. 99–101, Springer Verlag, London, U.K. (2001)
4. Choi, S. W. and I.-B. Lee; “Nonlinear Dynamic Process Monitoring Based on Dynamic Kernel PCA,” Chem. Eng. Sci., 59, 5897–5908 (2004)
5. Cui, P., J. Li and G. Wang; “Improved Kernel Principal Component Analysis for Fault Detection,” Expert Syst. Appl., 34, 1210–1219 (2008)
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